prompts stringlengths 81 413 | metrics_response stringlengths 0 371 |
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What metrics were used to measure the VGG8B + LocalLearning + CO model in the Training Neural Networks with Local Error Signals paper on the STL-10 dataset? | Percentage correct, FLOPS, PARAMS |
What metrics were used to measure the ResNet18(GN, 4) model in the Extended Batch Normalization paper on the STL-10 dataset? | Percentage correct, FLOPS, PARAMS |
What metrics were used to measure the PSLR-Linear model in the Probabilistic Structural Latent Representation for Unsupervised Embedding paper on the STL-10 dataset? | Percentage correct, FLOPS, PARAMS |
What metrics were used to measure the ResNet18(BN, 128) model in the Extended Batch Normalization paper on the STL-10 dataset? | Percentage correct, FLOPS, PARAMS |
What metrics were used to measure the Mean Teacher model in the FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence paper on the STL-10 dataset? | Percentage correct, FLOPS, PARAMS |
What metrics were used to measure the CPC† model in the A Framework For Contrastive Self-Supervised Learning And Designing A New Approach paper on the STL-10 dataset? | Percentage correct, FLOPS, PARAMS |
What metrics were used to measure the Hamiltonian model in the Deep Neural Networks Motivated by Partial Differential Equations paper on the STL-10 dataset? | Percentage correct, FLOPS, PARAMS |
What metrics were used to measure the CC-GAN² model in the Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks paper on the STL-10 dataset? | Percentage correct, FLOPS, PARAMS |
What metrics were used to measure the CC-GAN model in the ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring paper on the STL-10 dataset? | Percentage correct, FLOPS, PARAMS |
What metrics were used to measure the Parabolic model in the Deep Neural Networks Motivated by Partial Differential Equations paper on the STL-10 dataset? | Percentage correct, FLOPS, PARAMS |
What metrics were used to measure the Scat + WRN 20-8 model in the Scaling the Scattering Transform: Deep Hybrid Networks paper on the STL-10 dataset? | Percentage correct, FLOPS, PARAMS |
What metrics were used to measure the ResNet18(EBN, 4) model in the Extended Batch Normalization paper on the STL-10 dataset? | Percentage correct, FLOPS, PARAMS |
What metrics were used to measure the Exemplar CNN model in the Scaling the Scattering Transform: Deep Hybrid Networks paper on the STL-10 dataset? | Percentage correct, FLOPS, PARAMS |
What metrics were used to measure the ResNet18(EBN, 128) model in the Extended Batch Normalization paper on the STL-10 dataset? | Percentage correct, FLOPS, PARAMS |
What metrics were used to measure the Stacked what-where AE model in the Scaling the Scattering Transform: Deep Hybrid Networks paper on the STL-10 dataset? | Percentage correct, FLOPS, PARAMS |
What metrics were used to measure the SWWAE model in the HybridNet: Classification and Reconstruction Cooperation for Semi-Supervised Learning paper on the STL-10 dataset? | Percentage correct, FLOPS, PARAMS |
What metrics were used to measure the SWWAE model in the ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring paper on the STL-10 dataset? | Percentage correct, FLOPS, PARAMS |
What metrics were used to measure the SWWAE model in the Stacked What-Where Auto-encoders paper on the STL-10 dataset? | Percentage correct, FLOPS, PARAMS |
What metrics were used to measure the Second-order model in the Deep Neural Networks Motivated by Partial Differential Equations paper on the STL-10 dataset? | Percentage correct, FLOPS, PARAMS |
What metrics were used to measure the Convolutional Clustering model in the Convolutional Clustering for Unsupervised Learning paper on the STL-10 dataset? | Percentage correct, FLOPS, PARAMS |
What metrics were used to measure the Π-Model model in the FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence paper on the STL-10 dataset? | Percentage correct, FLOPS, PARAMS |
What metrics were used to measure the Discriminative Unsupervised Feature Learning with Convolutional Neural Networks model in the Discriminative Unsupervised Feature Learning with Convolutional Neural Networks paper on the STL-10 dataset? | Percentage correct, FLOPS, PARAMS |
What metrics were used to measure the ResNet18(GN, 128) model in the Extended Batch Normalization paper on the STL-10 dataset? | Percentage correct, FLOPS, PARAMS |
What metrics were used to measure the Pseudo-Labeling model in the FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence paper on the STL-10 dataset? | Percentage correct, FLOPS, PARAMS |
What metrics were used to measure the Entropy model in the Increasing Trustworthiness of Deep Neural Networks via Accuracy Monitoring paper on the STL-10 dataset? | Percentage correct, FLOPS, PARAMS |
What metrics were used to measure the BDW model in the Don’t Wait, Just Weight: Improving Unsupervised Representations by Learning Goal-Driven Instance Weights paper on the STL-10 dataset? | Percentage correct, FLOPS, PARAMS |
What metrics were used to measure the MP model in the Increasing Trustworthiness of Deep Neural Networks via Accuracy Monitoring paper on the STL-10 dataset? | Percentage correct, FLOPS, PARAMS |
What metrics were used to measure the WaveMixLite-256/7 model in the WaveMix: A Resource-efficient Neural Network for Image Analysis paper on the STL-10 dataset? | Percentage correct, FLOPS, PARAMS |
What metrics were used to measure the CNN model in the Scaling the Scattering Transform: Deep Hybrid Networks paper on the STL-10 dataset? | Percentage correct, FLOPS, PARAMS |
What metrics were used to measure the An Analysis of Unsupervised Pre-training in Light of Recent Advances model in the An Analysis of Unsupervised Pre-training in Light of Recent Advances paper on the STL-10 dataset? | Percentage correct, FLOPS, PARAMS |
What metrics were used to measure the Multi-Task Bayesian Optimization model in the Multi-Task Bayesian Optimization paper on the STL-10 dataset? | Percentage correct, FLOPS, PARAMS |
What metrics were used to measure the NN-Weighter model in the Don’t Wait, Just Weight: Improving Unsupervised Representations by Learning Goal-Driven Instance Weights paper on the STL-10 dataset? | Percentage correct, FLOPS, PARAMS |
What metrics were used to measure the Accuracy Monitoring model in the Increasing Trustworthiness of Deep Neural Networks via Accuracy Monitoring paper on the STL-10 dataset? | Percentage correct, FLOPS, PARAMS |
What metrics were used to measure the C-SVDDNet model in the Unsupervised Feature Learning with C-SVDDNet paper on the STL-10 dataset? | Percentage correct, FLOPS, PARAMS |
What metrics were used to measure the RotNet model in the Don’t Wait, Just Weight: Improving Unsupervised Representations by Learning Goal-Driven Instance Weights paper on the STL-10 dataset? | Percentage correct, FLOPS, PARAMS |
What metrics were used to measure the DFF Committees model in the Committees of deep feedforward networks trained with few data paper on the STL-10 dataset? | Percentage correct, FLOPS, PARAMS |
What metrics were used to measure the Hierarchical Matching Pursuit (HMP) model in the Scaling the Scattering Transform: Deep Hybrid Networks paper on the STL-10 dataset? | Percentage correct, FLOPS, PARAMS |
What metrics were used to measure the L2RW model in the Don’t Wait, Just Weight: Improving Unsupervised Representations by Learning Goal-Driven Instance Weights paper on the STL-10 dataset? | Percentage correct, FLOPS, PARAMS |
What metrics were used to measure the Discriminative Learning of Sum-Product Networks model in the Discriminative Learning of Sum-Product Networks paper on the STL-10 dataset? | Percentage correct, FLOPS, PARAMS |
What metrics were used to measure the CKN model in the Convolutional Kernel Networks paper on the STL-10 dataset? | Percentage correct, FLOPS, PARAMS |
What metrics were used to measure the S-CNN model in the Selective Unsupervised Feature Learning with Convolutional Neural Network (S-CNN) paper on the STL-10 dataset? | Percentage correct, FLOPS, PARAMS |
What metrics were used to measure the Simulated Fixations model in the A Framework For Contrastive Self-Supervised Learning And Designing A New Approach paper on the STL-10 dataset? | Percentage correct, FLOPS, PARAMS |
What metrics were used to measure the No more meta-parameter tuning in unsupervised sparse feature learning model in the No more meta-parameter tuning in unsupervised sparse feature learning paper on the STL-10 dataset? | Percentage correct, FLOPS, PARAMS |
What metrics were used to measure the Convolutional K-means Network model in the Scaling the Scattering Transform: Deep Hybrid Networks paper on the STL-10 dataset? | Percentage correct, FLOPS, PARAMS |
What metrics were used to measure the Receptive Fields model in the Receptive Fields without Spike-Triggering paper on the STL-10 dataset? | Percentage correct, FLOPS, PARAMS |
What metrics were used to measure the PWD model in the Effective Version Space Reduction for Convolutional Neural Networks paper on the STL-10 dataset? | Percentage correct, FLOPS, PARAMS |
What metrics were used to measure the GVD model in the Effective Version Space Reduction for Convolutional Neural Networks paper on the STL-10 dataset? | Percentage correct, FLOPS, PARAMS |
What metrics were used to measure the VR model in the Effective Version Space Reduction for Convolutional Neural Networks paper on the STL-10 dataset? | Percentage correct, FLOPS, PARAMS |
What metrics were used to measure the Core SET model in the Effective Version Space Reduction for Convolutional Neural Networks paper on the STL-10 dataset? | Percentage correct, FLOPS, PARAMS |
What metrics were used to measure the GE model in the Effective Version Space Reduction for Convolutional Neural Networks paper on the STL-10 dataset? | Percentage correct, FLOPS, PARAMS |
What metrics were used to measure the DFAL model in the Effective Version Space Reduction for Convolutional Neural Networks paper on the STL-10 dataset? | Percentage correct, FLOPS, PARAMS |
What metrics were used to measure the Random model in the Effective Version Space Reduction for Convolutional Neural Networks paper on the STL-10 dataset? | Percentage correct, FLOPS, PARAMS |
What metrics were used to measure the BALD-MCD model in the Effective Version Space Reduction for Convolutional Neural Networks paper on the STL-10 dataset? | Percentage correct, FLOPS, PARAMS |
What metrics were used to measure the Sign-symmetry model in the How Important is Weight Symmetry in Backpropagation? paper on the STL-10 dataset? | Percentage correct, FLOPS, PARAMS |
What metrics were used to measure the M2-PWD model in the Effective Version Space Reduction for Convolutional Neural Networks paper on the STL-10 dataset? | Percentage correct, FLOPS, PARAMS |
What metrics were used to measure the soft ica model in the ICA with Reconstruction Cost for Efficient Overcomplete Feature Learning paper on the STL-10 dataset? | Percentage correct, FLOPS, PARAMS |
What metrics were used to measure the WaveMix-256/16 (level 2) model in the WaveMix: A Resource-efficient Neural Network for Image Analysis paper on the iNat2021-mini dataset? | Top 1 Accuracy |
What metrics were used to measure the LRA-diffusion (CLIP ViT) model in the Label-Retrieval-Augmented Diffusion Models for Learning from Noisy Labels paper on the mini WebVision 1.0 dataset? | Top-1 Accuracy, Top-5 Accuracy, ImageNet Top-1 Accuracy, ImageNet Top-5 Accuracy |
What metrics were used to measure the Robust LR model in the Two Wrongs Don't Make a Right: Combating Confirmation Bias in Learning with Label Noise paper on the mini WebVision 1.0 dataset? | Top-1 Accuracy, Top-5 Accuracy, ImageNet Top-1 Accuracy, ImageNet Top-5 Accuracy |
What metrics were used to measure the PGDF (Inception-ResNet-v2) model in the Sample Prior Guided Robust Model Learning to Suppress Noisy Labels paper on the mini WebVision 1.0 dataset? | Top-1 Accuracy, Top-5 Accuracy, ImageNet Top-1 Accuracy, ImageNet Top-5 Accuracy |
What metrics were used to measure the SSR model in the SSR: An Efficient and Robust Framework for Learning with Unknown Label Noise paper on the mini WebVision 1.0 dataset? | Top-1 Accuracy, Top-5 Accuracy, ImageNet Top-1 Accuracy, ImageNet Top-5 Accuracy |
What metrics were used to measure the BtR model in the Bootstrapping the Relationship Between Images and Their Clean and Noisy Labels paper on the mini WebVision 1.0 dataset? | Top-1 Accuracy, Top-5 Accuracy, ImageNet Top-1 Accuracy, ImageNet Top-5 Accuracy |
What metrics were used to measure the CoDiM-Sup (Inception-ResNet-v2) model in the CoDiM: Learning with Noisy Labels via Contrastive Semi-Supervised Learning paper on the mini WebVision 1.0 dataset? | Top-1 Accuracy, Top-5 Accuracy, ImageNet Top-1 Accuracy, ImageNet Top-5 Accuracy |
What metrics were used to measure the NCR+Mixup+DA (ResNet-50) model in the Learning with Neighbor Consistency for Noisy Labels paper on the mini WebVision 1.0 dataset? | Top-1 Accuracy, Top-5 Accuracy, ImageNet Top-1 Accuracy, ImageNet Top-5 Accuracy |
What metrics were used to measure the CMW-Net-SL+C2D model in the CMW-Net: Learning a Class-Aware Sample Weighting Mapping for Robust Deep Learning paper on the mini WebVision 1.0 dataset? | Top-1 Accuracy, Top-5 Accuracy, ImageNet Top-1 Accuracy, ImageNet Top-5 Accuracy |
What metrics were used to measure the Dynamic Loss (Inception-ResNet-v2) model in the Dynamic Loss For Robust Learning paper on the mini WebVision 1.0 dataset? | Top-1 Accuracy, Top-5 Accuracy, ImageNet Top-1 Accuracy, ImageNet Top-5 Accuracy |
What metrics were used to measure the CoDiM-Self (Inception-ResNet-v2) model in the CoDiM: Learning with Noisy Labels via Contrastive Semi-Supervised Learning paper on the mini WebVision 1.0 dataset? | Top-1 Accuracy, Top-5 Accuracy, ImageNet Top-1 Accuracy, ImageNet Top-5 Accuracy |
What metrics were used to measure the Sel-CL+ (ResNet-18) model in the Selective-Supervised Contrastive Learning with Noisy Labels paper on the mini WebVision 1.0 dataset? | Top-1 Accuracy, Top-5 Accuracy, ImageNet Top-1 Accuracy, ImageNet Top-5 Accuracy |
What metrics were used to measure the CPC model in the Class Prototype-based Cleaner for Label Noise Learning paper on the mini WebVision 1.0 dataset? | Top-1 Accuracy, Top-5 Accuracy, ImageNet Top-1 Accuracy, ImageNet Top-5 Accuracy |
What metrics were used to measure the DivideMix with C2D (ResNet-50) model in the Contrast to Divide: Self-Supervised Pre-Training for Learning with Noisy Labels paper on the mini WebVision 1.0 dataset? | Top-1 Accuracy, Top-5 Accuracy, ImageNet Top-1 Accuracy, ImageNet Top-5 Accuracy |
What metrics were used to measure the FaMUS model in the Faster Meta Update Strategy for Noise-Robust Deep Learning paper on the mini WebVision 1.0 dataset? | Top-1 Accuracy, Top-5 Accuracy, ImageNet Top-1 Accuracy, ImageNet Top-5 Accuracy |
What metrics were used to measure the NCR+Mixup (ResNet-50) model in the Learning with Neighbor Consistency for Noisy Labels paper on the mini WebVision 1.0 dataset? | Top-1 Accuracy, Top-5 Accuracy, ImageNet Top-1 Accuracy, ImageNet Top-5 Accuracy |
What metrics were used to measure the CC model in the Centrality and Consistency: Two-Stage Clean Samples Identification for Learning with Instance-Dependent Noisy Labels paper on the mini WebVision 1.0 dataset? | Top-1 Accuracy, Top-5 Accuracy, ImageNet Top-1 Accuracy, ImageNet Top-5 Accuracy |
What metrics were used to measure the GJS (ResNet-50) model in the Generalized Jensen-Shannon Divergence Loss for Learning with Noisy Labels paper on the mini WebVision 1.0 dataset? | Top-1 Accuracy, Top-5 Accuracy, ImageNet Top-1 Accuracy, ImageNet Top-5 Accuracy |
What metrics were used to measure the NGC (Inception-ResNet-v2) model in the NGC: A Unified Framework for Learning with Open-World Noisy Data paper on the mini WebVision 1.0 dataset? | Top-1 Accuracy, Top-5 Accuracy, ImageNet Top-1 Accuracy, ImageNet Top-5 Accuracy |
What metrics were used to measure the TCL model in the Twin Contrastive Learning with Noisy Labels paper on the mini WebVision 1.0 dataset? | Top-1 Accuracy, Top-5 Accuracy, ImageNet Top-1 Accuracy, ImageNet Top-5 Accuracy |
What metrics were used to measure the LongReMix (Inception-ResNet-v2) model in the LongReMix: Robust Learning with High Confidence Samples in a Noisy Label Environment paper on the mini WebVision 1.0 dataset? | Top-1 Accuracy, Top-5 Accuracy, ImageNet Top-1 Accuracy, ImageNet Top-5 Accuracy |
What metrics were used to measure the MOIT+ (ResNet-18) model in the Multi-Objective Interpolation Training for Robustness to Label Noise paper on the mini WebVision 1.0 dataset? | Top-1 Accuracy, Top-5 Accuracy, ImageNet Top-1 Accuracy, ImageNet Top-5 Accuracy |
What metrics were used to measure the CMW-Net-SL model in the CMW-Net: Learning a Class-Aware Sample Weighting Mapping for Robust Deep Learning paper on the mini WebVision 1.0 dataset? | Top-1 Accuracy, Top-5 Accuracy, ImageNet Top-1 Accuracy, ImageNet Top-5 Accuracy |
What metrics were used to measure the ELR+ (Inception-ResNet-v2) model in the Early-Learning Regularization Prevents Memorization of Noisy Labels paper on the mini WebVision 1.0 dataset? | Top-1 Accuracy, Top-5 Accuracy, ImageNet Top-1 Accuracy, ImageNet Top-5 Accuracy |
What metrics were used to measure the ScanMix (Inception-ResNet-v2) model in the ScanMix: Learning from Severe Label Noise via Semantic Clustering and Semi-Supervised Learning paper on the mini WebVision 1.0 dataset? | Top-1 Accuracy, Top-5 Accuracy, ImageNet Top-1 Accuracy, ImageNet Top-5 Accuracy |
What metrics were used to measure the ROLT+ (Inception-ResNet-v2) model in the Robust Long-Tailed Learning under Label Noise paper on the mini WebVision 1.0 dataset? | Top-1 Accuracy, Top-5 Accuracy, ImageNet Top-1 Accuracy, ImageNet Top-5 Accuracy |
What metrics were used to measure the CNLCU-S + DivideMix (Inception-ResNet-v2) model in the Sample Selection with Uncertainty of Losses for Learning with Noisy Labels paper on the mini WebVision 1.0 dataset? | Top-1 Accuracy, Top-5 Accuracy, ImageNet Top-1 Accuracy, ImageNet Top-5 Accuracy |
What metrics were used to measure the HSA-NRL(Inception-ResNet-v2) model in the Hard Sample Aware Noise Robust Learning for Histopathology Image Classification paper on the mini WebVision 1.0 dataset? | Top-1 Accuracy, Top-5 Accuracy, ImageNet Top-1 Accuracy, ImageNet Top-5 Accuracy |
What metrics were used to measure the CAR model in the Confidence Adaptive Regularization for Deep Learning with Noisy Labels paper on the mini WebVision 1.0 dataset? | Top-1 Accuracy, Top-5 Accuracy, ImageNet Top-1 Accuracy, ImageNet Top-5 Accuracy |
What metrics were used to measure the DivideMix (Inception-ResNet-v2) model in the DivideMix: Learning with Noisy Labels as Semi-supervised Learning paper on the mini WebVision 1.0 dataset? | Top-1 Accuracy, Top-5 Accuracy, ImageNet Top-1 Accuracy, ImageNet Top-5 Accuracy |
What metrics were used to measure the NCR (ResNet-50) model in the Learning with Neighbor Consistency for Noisy Labels paper on the mini WebVision 1.0 dataset? | Top-1 Accuracy, Top-5 Accuracy, ImageNet Top-1 Accuracy, ImageNet Top-5 Accuracy |
What metrics were used to measure the DivideMix (ResNet-50) model in the DivideMix: Learning with Noisy Labels as Semi-supervised Learning paper on the mini WebVision 1.0 dataset? | Top-1 Accuracy, Top-5 Accuracy, ImageNet Top-1 Accuracy, ImageNet Top-5 Accuracy |
What metrics were used to measure the DivideMix (ResNet-18) model in the DivideMix: Learning with Noisy Labels as Semi-supervised Learning paper on the mini WebVision 1.0 dataset? | Top-1 Accuracy, Top-5 Accuracy, ImageNet Top-1 Accuracy, ImageNet Top-5 Accuracy |
What metrics were used to measure the MentorMix (Inception-ResNet-v2) model in the Beyond Synthetic Noise: Deep Learning on Controlled Noisy Labels paper on the mini WebVision 1.0 dataset? | Top-1 Accuracy, Top-5 Accuracy, ImageNet Top-1 Accuracy, ImageNet Top-5 Accuracy |
What metrics were used to measure the NCT (Inception-ResNet-v2) model in the Noisy Concurrent Training for Efficient Learning under Label Noise paper on the mini WebVision 1.0 dataset? | Top-1 Accuracy, Top-5 Accuracy, ImageNet Top-1 Accuracy, ImageNet Top-5 Accuracy |
What metrics were used to measure the ODD (Inception-ResNet-v2) model in the Robust and On-the-fly Dataset Denoising for Image Classification paper on the mini WebVision 1.0 dataset? | Top-1 Accuracy, Top-5 Accuracy, ImageNet Top-1 Accuracy, ImageNet Top-5 Accuracy |
What metrics were used to measure the Crust (Inception-ResNet-v2) model in the Coresets for Robust Training of Neural Networks against Noisy Labels paper on the mini WebVision 1.0 dataset? | Top-1 Accuracy, Top-5 Accuracy, ImageNet Top-1 Accuracy, ImageNet Top-5 Accuracy |
What metrics were used to measure the Iterative-CV (Inception-ResNet-v2) model in the Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels paper on the mini WebVision 1.0 dataset? | Top-1 Accuracy, Top-5 Accuracy, ImageNet Top-1 Accuracy, ImageNet Top-5 Accuracy |
What metrics were used to measure the Co-teaching (Inception-ResNet-v2) model in the Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels paper on the mini WebVision 1.0 dataset? | Top-1 Accuracy, Top-5 Accuracy, ImageNet Top-1 Accuracy, ImageNet Top-5 Accuracy |
What metrics were used to measure the D2L (Inception-ResNet-v2) model in the Dimensionality-Driven Learning with Noisy Labels paper on the mini WebVision 1.0 dataset? | Top-1 Accuracy, Top-5 Accuracy, ImageNet Top-1 Accuracy, ImageNet Top-5 Accuracy |
What metrics were used to measure the F-Correction (Inception-ResNet-v2) model in the Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach paper on the mini WebVision 1.0 dataset? | Top-1 Accuracy, Top-5 Accuracy, ImageNet Top-1 Accuracy, ImageNet Top-5 Accuracy |
What metrics were used to measure the RTE (Inception-ResNet-v2) model in the Robust Temporal Ensembling for Learning with Noisy Labels paper on the mini WebVision 1.0 dataset? | Top-1 Accuracy, Top-5 Accuracy, ImageNet Top-1 Accuracy, ImageNet Top-5 Accuracy |
What metrics were used to measure the MentorNet (Inception-ResNet-v2) model in the MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels paper on the mini WebVision 1.0 dataset? | Top-1 Accuracy, Top-5 Accuracy, ImageNet Top-1 Accuracy, ImageNet Top-5 Accuracy |
What metrics were used to measure the NCE+RCE (ResNet-50) model in the Normalized Loss Functions for Deep Learning with Noisy Labels paper on the mini WebVision 1.0 dataset? | Top-1 Accuracy, Top-5 Accuracy, ImageNet Top-1 Accuracy, ImageNet Top-5 Accuracy |
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